Cursor Agent
48246f31bd
Based on manvalan/pinscope main. Default LLM is DeepSeek with local skills and PDF ingest. Datasheets are fetched from LCSC/TI, stored in the component library, and review extracts abs-max with a deeper checklist. Adds scripts/update-pinscope.sh for the production host.
92 lines
4.5 KiB
Python
92 lines
4.5 KiB
Python
"""Per-provider pricing tables and cost computation.
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Replaces the flat ``PRICING`` dict that used to live in
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``backend/services/api_logs.py``. Indexed by (provider, model).
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"""
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from __future__ import annotations
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# Per-million-token USD rates. Source-of-truth links:
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# DeepSeek: https://api-docs.deepseek.com/quick_start/pricing
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# Anthropic: https://docs.anthropic.com/en/docs/about-claude/pricing
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# Google: https://ai.google.dev/pricing
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# Last updated: 2026-08-27
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PRICING: dict[str, dict[str, dict[str, float]]] = {
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"deepseek": {
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# Peak-hour rates (conservative). Off-peak is 50% of these.
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# Cache-hit input is billed via CACHE_RATES["deepseek"]["read"].
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"deepseek-v4-flash": {"input": 0.44, "output": 1.32},
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"deepseek-v4-flash-vision-exp": {"input": 0.44, "output": 1.32},
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"deepseek-v4-pro": {"input": 1.32, "output": 3.96},
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"default": {"input": 0.44, "output": 1.32},
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},
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"anthropic": {
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"claude-opus-4-6": {"input": 5.00, "output": 25.00},
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"claude-opus-4-5": {"input": 5.00, "output": 25.00},
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"claude-opus-4-1": {"input": 15.00, "output": 75.00},
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"claude-opus-4": {"input": 15.00, "output": 75.00},
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"claude-sonnet-4-6": {"input": 3.00, "output": 15.00},
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# Sonnet 5 standard rate (== Sonnet 4.6). Introductory pricing of
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# $2/$10 runs through 2026-08-31; intentionally NOT tracked here —
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# chosen set-and-forget so no dated bump is needed on 2026-09-01.
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# (New tokenizer emits ~30% more tokens, so per-run cost still rises.)
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"claude-sonnet-5": {"input": 3.00, "output": 15.00},
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"claude-sonnet-4-5": {"input": 3.00, "output": 15.00},
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"claude-sonnet-4": {"input": 3.00, "output": 15.00},
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"claude-haiku-4-5-20251001": {"input": 1.00, "output": 5.00},
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"claude-haiku-4-5": {"input": 1.00, "output": 5.00},
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"claude-haiku-3-5": {"input": 0.80, "output": 4.00},
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"default": {"input": 3.00, "output": 15.00},
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},
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"gemini": {
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# Gemini 3 Flash pricing (per 1M tokens). Preview alias mirrors GA.
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"gemini-3-flash-preview": {"input": 0.30, "output": 2.50},
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"gemini-3-flash": {"input": 0.30, "output": 2.50},
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"gemini-flash-latest": {"input": 0.30, "output": 2.50},
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"gemini-2.5-flash": {"input": 0.30, "output": 2.50},
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"gemini-2.5-pro": {"input": 1.25, "output": 10.00},
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# Gemini 3.1 Pro Preview — standard tier, prompts ≤200k tokens.
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# Above 200k Google charges $4.00/$18.00; we don't yet split by
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# prompt size, so we use the smaller-tier rate. Almost every
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# pipeline call here is well under 200k.
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"gemini-3.1-pro-preview": {"input": 2.00, "output": 12.00},
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"gemini-3-pro-preview": {"input": 2.00, "output": 12.00},
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"default": {"input": 0.30, "output": 2.50},
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},
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}
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# Per-provider cache token multipliers, applied on top of the input rate.
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# create: cost when a cache is *written* (Anthropic charges 1.25× input;
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# Gemini charges 1.0× input — caching writes are billed as a
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# normal input pass)
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# read: cost when a cached prefix is *reused* (much cheaper)
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CACHE_RATES: dict[str, dict[str, float]] = {
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"deepseek": {"create": 1.00, "read": 0.032},
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"anthropic": {"create": 1.25, "read": 0.10},
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"gemini": {"create": 1.00, "read": 0.25},
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}
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def cost_for_entry(entry: dict) -> float:
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"""USD cost for an api_logs entry. Reads ``provider`` (default
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``anthropic`` for legacy entries) and ``model`` to pick rates."""
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provider = entry.get("provider") or "deepseek"
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table = PRICING.get(provider) or PRICING["deepseek"]
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rates = table.get(entry.get("model", ""), table["default"])
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cache_rates = CACHE_RATES.get(provider, CACHE_RATES["deepseek"])
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input_rate = rates["input"]
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output_rate = rates["output"]
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return (
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entry.get("input_tokens", 0) * input_rate
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+ entry.get("cache_creation_input_tokens", 0) * input_rate * cache_rates["create"]
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+ entry.get("cache_read_input_tokens", 0) * input_rate * cache_rates["read"]
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+ entry.get("output_tokens", 0) * output_rate
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) / 1_000_000
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def total_cost(entries: list[dict]) -> float:
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"""Sum USD across entries."""
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return round(sum(cost_for_entry(e) for e in entries), 6)
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